This disclosure relates to digital image filtering, based on applying 1D convolution kernels to 1D local histogram data, and is thereby very different from the conventional idea of applying 2D convolution kernels to 2D image data.
One major application of this image filtering domain is to enhance local contrast in an image. One conventional approach of doing this is to spread the values of the image signal so that the lowest appearing value is forced to 0.0 and the highest appearing value is forced to 1.0. If this is done for the entire image, this is an old, well-established approach. This can also be done locally, that is tile-by-tile, which is also not a novel approach. Another approach tries to find a local upper bound and a local lower bound of the image signal, which can be understood as a locally dependent “lowest value” and a locally dependent “highest value”, so that the image signal is then spread so that the lower boundary is set to 0.0 and the upper boundary is set to 1.0. The downside is that this system creates artifacting and may take a long time to process.
There is a high demand in the field of image processing for edge preserving smoothing filters. An old, known filter of this kind is the median filter, as it leaves strong edges in the image without blurring them, while small details are erased. Another representative filter of the class of edge preserving smoothing is the bilateral filter, which is a derivation of the Gaussian blur filter. It has a wide usage for photographic and analytic image processing. Its downside is that a fast and efficient implementation is not easily possible.